Paper Number
ECIS2025-1331
Paper Type
CRP
Abstract
Physical AI refers to Artificial Intelligence (AI) systems embedded in tangible devices that interact with the physical world through sensors and actuators. This paper explores the concept of Physical AI in the context of automated driving, a prominent example of its application. By integrating AI into vehicles, these systems enhance driving while still requiring human oversight. Through a theoretical lens of trust in automation, the paper examines how users develop and calibrate their trust in AI-powered systems, using qualitative data collected through think-aloud protocols in a real-world driving study with 100 non-professional drivers. The findings reveal four key user archetypes of Physical AI—Critical Users, Enthusiastic Adopters, Hesitant Sceptics, and Passive Believers—each representing varying levels of trust and engagement with Physical AI. These archetypes offer valuable insights into human-Physical AI collaboration, underscoring the critical role of trust and engagement in shaping the adoption of Physical AI.
Recommended Citation
Stocker, Alexander, "USER ARCHETYPES OF PHYSICAL AI SYSTEMS: INSIGHTS FROM AN AUTOMATED DRIVING SYSTEMS FIELD STUDY" (2025). ECIS 2025 Proceedings. 14.
https://aisel.aisnet.org/ecis2025/ai_org/ai_org/14
USER ARCHETYPES OF PHYSICAL AI SYSTEMS: INSIGHTS FROM AN AUTOMATED DRIVING SYSTEMS FIELD STUDY
Physical AI refers to Artificial Intelligence (AI) systems embedded in tangible devices that interact with the physical world through sensors and actuators. This paper explores the concept of Physical AI in the context of automated driving, a prominent example of its application. By integrating AI into vehicles, these systems enhance driving while still requiring human oversight. Through a theoretical lens of trust in automation, the paper examines how users develop and calibrate their trust in AI-powered systems, using qualitative data collected through think-aloud protocols in a real-world driving study with 100 non-professional drivers. The findings reveal four key user archetypes of Physical AI—Critical Users, Enthusiastic Adopters, Hesitant Sceptics, and Passive Believers—each representing varying levels of trust and engagement with Physical AI. These archetypes offer valuable insights into human-Physical AI collaboration, underscoring the critical role of trust and engagement in shaping the adoption of Physical AI.
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